Dutch deepfake detection firm DuckDuckGoose partners with Latin America's largest identity hub to block over 500,000 synthetic identities, marking a major enterprise deployment of AI-generated fraud prevention.
OpenAI confirms historic $110 billion funding round with Amazon contributing $50B and Nvidia and SoftBank adding $30B, marking the largest private funding deal ever.
New research combines reinforcement learning with knowledge distillation to improve how smaller language models learn complex reasoning from larger teacher models.
New research proposes formal specification methods and runtime enforcement mechanisms to ensure autonomous AI agents behave reliably and predictably in real-world deployments.
New research introduces AutoQRA, a framework that jointly optimizes mixed-precision quantization and low-rank adapters, enabling more efficient fine-tuning of large language models on limited hardware.
A developer's deep dive into creating SlotBot, an AI agent that mimics solo business owners for scheduling tasks, revealing key lessons about agentic system architecture and the future of AI impersonation.
Voice authentication leader Pindrop expands its AI-powered deepfake detection technology into healthcare, addressing growing synthetic voice fraud threats targeting patient data and medical systems.
Voice AI leader ElevenLabs will leverage Google Cloud services powered by Nvidia chips, expanding its synthetic audio infrastructure for next-generation voice cloning and generation.
New research introduces Constricting Barrier Functions for mathematically guaranteed safe outputs from generative AI models, offering formal safety proofs for controlled content generation.
New research introduces MINAR framework for understanding how neural networks learn to execute algorithms, advancing interpretability methods critical for AI safety and verification.
Researchers propose combining self-consistency sampling with conformal calibration to certify AI agent reliability without requiring access to internal model weights or architecture details.
New research introduces Tool-R0, a framework enabling LLM agents to autonomously learn tool usage through self-evolution, eliminating the need for curated training datasets while achieving state-of-the-art performance.